Modeling Spatial Risk Variation of Aftershocks Using Zero-Inflated Poisson Model
碩士 === 國立彰化師範大學 === 統計資訊研究所 === 106 === Modeling spatial risk variation of the interested event is an active research topic in spatial statistics. For count data, when response variables are collected with the excessive zero values, the traditional Poisson regression model may be not suitable for an...
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| Format: | Others |
| Language: | en_US |
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2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/zbnnvs |
| Summary: | 碩士 === 國立彰化師範大學 === 統計資訊研究所 === 106 === Modeling spatial risk variation of the interested event is an active research topic in spatial statistics. For count data, when response variables are collected with the excessive zero values, the traditional Poisson regression model may be not suitable for analyzing this type of data. To overcome this issue, we use the zero-inflated Poisson model combined with the spatial hierarchical Bayesian model to assess the spatial risk variation of the interested event, where the spatial correlations of the data set are modeled by the conditional autoregressive model and a logistic regression is used to spatially model the probabilistic variabilities of risks. The statistical inferences of model parameters and risk assessments are conducted based on Bayesian frameworks. We use a real data set regarding aftershocks of 921 Chi-Chi earthquake in Taiwan to illustrate the effectiveness of the proposed methodology.
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